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author:

Chen, C.-H. (Chen, C.-H..) [1] | Zhang, Y. (Zhang, Y..) [2] | Guo, W. (Guo, W..) [3] | Pan, M. (Pan, M..) [4] | Lyu, L. (Lyu, L..) [5] | Lin, C.-Y. (Lin, C.-Y..) [6]

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Abstract:

This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%. © 2020 ACM.

Keyword:

contour accentuation; convolutional neural network; ship recognition; transfer learning

Community:

  • [ 1 ] [Chen, C.-H.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Zhang, Y.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 3 ] [Guo, W.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 4 ] [Pan, M.]Navigation College, Dalian Maritime University, Dalian, China
  • [ 5 ] [Lyu, L.]School of Computing, National University of Singapore, Singapore, Singapore
  • [ 6 ] [Lin, C.-Y.]Department of Electrical and Computer Engineering, National Chiao Tung University Hsinchu, Taiwan

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Source :

The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Year: 2020

Page: 61-62

Language: English

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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